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<title>Poisson Binomial Test</title>
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              <li class="active"><a href="#">Produce a report</a></li>
              <li><a href="fileFormat.html">File Format</a></li>
              <li><a href="assumptions.html">Experimental Setup</a></li>
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	<h2>Poisson Binomial Test</h2>
	<p>
	The Poisson binomial test is a tool designed to compare machine learning algorithms on multiple datasets to answer the following question : "Does learning algorithm A have a higher chance of producing a better predictor than learning algorithm B in the given context?". This helps you determine if your new algorithm is better than some state of the art algorithms. But, most importantly, it tells you if you have enough data to produce such a conclusion. For more information on this test, please read the following paper. 
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	<p>
	<b>Bayesian Comparison of Machine Learning Algorithms on Single and Multiple Datasets </b></br>
	Alexandre Lacoste, François Laviolette, Mario Marchand </br>
	<i>AISTATS 2012,  JMLR W&CP 22: 665-675</i>
	[<a href="http://jmlr.csail.mit.edu/proceedings/papers/v22/lacoste12/lacoste12.pdf">pdf</a>]
	</p>

	<P>
	This website is a service that produces a report based on all your testing data using the Poisson binomial test. If you would like to directly use the open source code instead of this service, please visit <a href="http://code.google.com/p/mleval/">mlEval</a> on google code.
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	<h2>How to use it</h2>

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	<li>Write your testing information in a loss file according to the <a href="fileFormat.html">required format</a> </li>
	<li>Make sure your experiment meets the <a href="assumptions.html">assumptions</a></li>
	<li>Submit your file</li>
	<li>Download the report</li>
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	<h2>Upload your loss file : </h2>
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